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Journal Of Korean Biblia Society for Library and Information Science

  • P-ISSN1229-2435
  • E-ISSN2799-4767
  • KCI

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Vol.32 No.3

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Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques

Journal Of Korean Biblia Society for Library and Information Science / Journal Of Korean Biblia Society for Library and Information Science, (P)1229-2435; (E)2799-4767
2021, v.32 no.3, pp.247-264
https://doi.org/10.14699/kbiblia.2021.32.3.247
Youngsoo Ko
Ju Hee Lee
Min Song
Ko, Y., Lee, J. H., & Song, M. (2021). Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques. , 32(3), 247-264, https://doi.org/10.14699/kbiblia.2021.32.3.247

Abstract

This study aims to create a deep learning-based classification model to classify suicide tendency by suicide corpus constructed for the present study. Also, to analyze suicide factors, the study classified suicide tendency corpus into detailed topics by using topic modeling, an analysis technique that automatically extracts topics. For this purpose, 2,011 documents of the suicide-related corpus collected from social media naver knowledge iN were directly annotated into suicide-tendency documents or non-suicide-tendency documents based on suicide prevention education manual issued by the Central Suicide Prevention Center, and we also conducted the deep learning model(LSTM, BERT, ELECTRA) performance evaluation based on the classification model, using annotated corpus data. In addition, one of the topic modeling techniques, LDA identified suicide factors by classifying thematic literature, and co-word analysis and visualization were conducted to analyze the factors in-depth.

keywords
자살, 소셜미디어, 단어 동시출현, 딥러닝, 토픽모델링, Suicide, Social media, Word Co-Occurrence, Deep-learning, Topic Modeling
Received
2021-08-16
Revised
2021-08-17
Accepted
2021-09-06
Published
2021-09-30

Journal Of Korean Biblia Society for Library and Information Science

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